Decision-making on Command Query Responsibility Segregation with Event Sourcing architectural variations
Oleksandr LYTVYNOV, Dmytro HRUZIN · Technology audit and production reserves · 2025
The object of the research is the process of selecting and evaluating architectural solutions, both at the design stage and during the migration of a software application’s architecture, within the context of evolutionary architecture. The paper is focused on variations of the Command Query Responsibility Segregation (CQRS) with Event Sourcing (ES) architecture, which, in fact, is a family of architectural variations that differ in complexity, performance, development time, and the required expertise from developers. These differences have a significant impact on the development cost and maintainability of the software application. Moreover, changes in business requirements or technical context often necessitate migration among architectural variations, which may drastically increase costs if not planned properly. In the absence of objective evaluation criteria, decisions are often based on expert judgment, which may be unavailable or insufficient. This work proposes a decision-making support approach for CQRS with ES architectural variation selection and migration planning. The approach is based on classification of processes and breaking them down into smaller activities. This enables objective comparisons of architectural variations based on complexity and performance metrics. The application of the approach is shown on two basic variations. Metrics were obtained, and a bitmap chart was built to visualize architectural applicability, depending on the project priorities. The applicability score of mCQRS ranges from 39% to 53%, while that of Classical CQRS – 47–61%. The proposed approach is applicable in projects where architecture evolution is expected. It is especially useful in organizations operating at Capability Maturity Models Integration (CMMI) Level 4 (Quantitatively Managed Organization) which is focused on predictability of quantitative performance improvement objectives.